It is often important to measure the quality of input data for a variety of reasons. For example, it can be beneficial to determine a quality of certain sound acquired in a system so that feedback can be provided to the source of that sound. That feedback can enable improvement of the sound at the source, enabling better communication of information in the future. Traditionally, such physical data extraction and analysis has utilized time-aggregated features (e.g., mean length of silence periods) to characterize the quality of the input data. Such, systems fail to take advantage of contextual information that can be acquired by looking at data, not only as a whole, but at individual segments within the data, in view of what has happened before and after those individual segments.